{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/neural-attentive-bag-of-entities-model-for","title":"Neural Attentive Bag-of-Entities Model for Text Classification","arxiv_id":"1909.01259","date":"2019-09-03","proceeding":"CONLL 2019 11","authors":["Ikuya Yamada","Hiroyuki Shindo"],"abstract":"This study proposes a Neural Attentive Bag-of-Entities model, which is a neural network model that performs text classification using entities in a knowledge base. Entities provide unambiguous and relevant semantic signals that are beneficial for capturing semantics in texts. We combine simple high-recall entity detection based on a dictionary, to detect entities in a document, with a novel neural attention mechanism that enables the model to focus on a small number of unambiguous and relevant entities. We tested the effectiveness of our model using two standard text classification datasets (i.e., the 20 Newsgroups and R8 datasets) and a popular factoid question answering dataset based on a trivia quiz game. As a result, our model achieved state-of-the-art results on all datasets. The source code of the proposed model is available online at https://github.com/wikipedia2vec/wikipedia2vec.","url_abs":"https://arxiv.org/abs/1909.01259v2","url_pdf":"https://arxiv.org/pdf/1909.01259v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"neural-attentive-bag-of-entities-model-for","repo_url":"https://github.com/wikipedia2vec/wikipedia2vec","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"neural-attentive-bag-of-entities-model-for","repo_url":"https://github.com/studio-ousia/wikipedia2vec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"neural-attentive-bag-of-entities-model-for","repo_url":"https://github.com/wikipedia2vec/wikipedia2vec/tree/master/examples/text_classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"text-classification","task_name":"Text Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/text-classification-on-20news","task":"Text Classification","dataset":"20NEWS","model":"NABoE-full","rank_in_archive_order":9,"of":16,"metrics":{"Accuracy":"86.8","F-measure":"86.2"},"uses_additional_data":false},{"leaderboard":"/sota/text-classification-on-r8","task":"Text Classification","dataset":"R8","model":"NABoE-full","rank_in_archive_order":16,"of":21,"metrics":{"Accuracy":"97.1","F-measure":"91.7"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1909.01259","atlas_url":"https://app.syntology.ai/?focus=1909.01259","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}